Trajectory optimization for UAV-enabled relaying with reinforcement learning  

作　　者：Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin 

机构地区：[1]School of Electronic and Information Engineering,Harbin Institute of Technology,Shenzhen,China [2]Guangdong Key Laboratory of Intelligent Information Processing,Shenzhen University,Shenzhen,China [3]Peng Cheng Laboratory(PCL),Shenzhen,China [4]National Mobile Communications Research Laboratory,Southeast University,Nanjing,China [5]Shenzhen Institute of Information Technology,Shenzhen,China [6]Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication,Shenzhen University,Shenzhen,China

出　　处：《Digital Communications and Networks》2025年第1期200-209,共10页数字通信与网络(英文版)

基　　金：supported in part by the Shenzhen Basic Research Project under Grant JCYJ20220531103008018 and Grant 20200812112423002;in part by the Guangdong Basic Research Program under Grant 2019A1515110358,2021A1515012097;in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University (No.2021D16)。

摘　　要：In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the UAV.To solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based approaches.Specifically,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL approach.The simulation results show that both approaches can solve the proposed optimization problem and obtain good results.Moreover,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关 键 词：Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]